P.048 Characterization of somatic mutations in mTOR pathway genes in focal cortical dysplasias
Bibliographic record
Abstract
Background: Focal cortical dysplasias (FCDs) are congenital structural abnormalities of the brain, and represent the most common cause of medication-resistant focal epilepsy in children and adults. Recent studies have shown that somatic mutations (i.e. mutations arising in the embryo) in mTOR pathway genes underlie some FCD cases. Specific therapies targeting the mTOR pathway are available. However, testing for somatic mTOR pathway mutations in FCD tissue is not performed on a clinical basis, and the contribution of such mutations to the pathogenesis of FCD remains unknown. Aim: To investigate the feasibility of screening for somatic mutations in resected FCD tissue and determine the proportion and spatial distribution of FCDs which are due to low-level somatic mTOR pathway mutations. Methods: We performed ultra-deep sequencing of 13 mTOR pathway genes using a custom HaloPlexHS target enrichment kit (Agilent Technologies) in 16 resected histologically-confirmed FCD specimens. Results: We identified causal variants in 62.5% (10/16) of patients at an alternate allele frequency of 0.75–33.7%. The spatial mutation frequency correlated with the FCD lesion’s size and severity. Conclusions: Screening FCD tissue using a custom panel results in a high yield, and should be considered clinically given the important potential implications regarding surgical resection, medical management and genetic counselling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".